
import pandas as pd 
import numpy as np

import seaborn as sns
import mglearn

%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import plotly.express as px

from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
warnings.warn('DelftStack')
warnings.warn('Do not show this message')


df = pd.read_csv("C:/Users/Juanjo/Escritorio/MasterDataScience/MachLern/practica/data/heartdisease.csv")
df.head()


df.isna().sum()

HeartDisease        0
BMI                 0
Smoking             0
AlcoholDrinking     0
Stroke              0
PhysicalHealth      0
MentalHealth        0
DiffWalking         0
Sex                 0
AgeCategory         0
Race                0
Diabetic            0
PhysicalActivity    0
GenHealth           0
SleepTime           0
Asthma              0
KidneyDisease       0
SkinCancer          0
dtype: int64


df.HeartDisease.value_counts()

No     292422
Yes     27373
Name: HeartDisease, dtype: int64


px.pie(df,names="HeartDisease")


plt.figure(figsize=(12,7))
sns.kdeplot(data=df,x="BMI",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='BMI', ylabel='Density'>


plt.figure(figsize=(12,7))
sns.kdeplot(data=df,x="SleepTime",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='SleepTime', ylabel='Density'>


plt.figure(figsize=(12,7))
sns.kdeplot(data=df,x="PhysicalHealth",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='PhysicalHealth', ylabel='Density'>


plt.figure(figsize=(12,7))
sns.kdeplot(data=df,x="MentalHealth",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='MentalHealth', ylabel='Density'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="Smoking",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='Smoking', ylabel='Count'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="Stroke",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='Stroke', ylabel='Count'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="DiffWalking",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='DiffWalking', ylabel='Count'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="Race",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='Race', ylabel='Count'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="Diabetic",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='Diabetic', ylabel='Count'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="PhysicalActivity",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='PhysicalActivity', ylabel='Count'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="Asthma",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='Asthma', ylabel='Count'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="KidneyDisease",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='KidneyDisease', ylabel='Count'>


plt.figure(figsize=(12,7))
sns.histplot(data=df,x="SkinCancer",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='SkinCancer', ylabel='Count'>


df.Race = df.Race.replace({"White":1, "Hispanic":2, "Black":3, "Other":4, "Asian":5, "American Indian/Alaskan Native":6})
df = df[df.columns].replace({"Yes":1, "No":0,"No, borderline diabetes":0, "Yes (during pregnancy)":1})
df = df[df.columns].replace({"Male":1, "Female":0})


df.AgeCategory = df.AgeCategory.replace({"18-24":0, "25-29":1, "30-34":2, "35-39":3, "40-44":4, "45-49":5,
                                       "50-54":6, "55-59":7, "60-64":8, "65-69":9, "70-74":10, "75-79": 11, "80 or older": 12})
df.AgeCategory.value_counts()

9     34151
8     33686
10    31065
7     29757
6     25382
12    24153
5     21791
11    21482
0     21064
4     21006
3     20550
2     18753
1     16955
Name: AgeCategory, dtype: int64


df.GenHealth = df.GenHealth.replace({"Excellent":0, "Very good":1, "Fair":2, "Good":3, "Poor":4})
df.GenHealth.value_counts()

1    113858
3     93129
0     66842
2     34677
4     11289
Name: GenHealth, dtype: int64


plt.figure(figsize=(15,10))
sns.kdeplot(data=df,x="GenHealth",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='GenHealth', ylabel='Density'>


plt.figure(figsize=(15,10))
sns.kdeplot(data=df,x="AgeCategory",hue="HeartDisease", multiple="stack")

<AxesSubplot:xlabel='AgeCategory', ylabel='Density'>


from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, average_precision_score, recall_score
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler, LabelEncoder, MinMaxScaler
from sklearn.compose import ColumnTransformer, make_column_selector


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 319795 entries, 0 to 319794
Data columns (total 18 columns):
 #   Column            Non-Null Count   Dtype  
---  ------            --------------   -----  
 0   HeartDisease      319795 non-null  int64  
 1   BMI               319795 non-null  float64
 2   Smoking           319795 non-null  int64  
 3   AlcoholDrinking   319795 non-null  int64  
 4   Stroke            319795 non-null  int64  
 5   PhysicalHealth    319795 non-null  float64
 6   MentalHealth      319795 non-null  float64
 7   DiffWalking       319795 non-null  int64  
 8   Sex               319795 non-null  int64  
 9   AgeCategory       319795 non-null  int64  
 10  Race              319795 non-null  int64  
 11  Diabetic          319795 non-null  int64  
 12  PhysicalActivity  319795 non-null  int64  
 13  GenHealth         319795 non-null  int64  
 14  SleepTime         319795 non-null  float64
 15  Asthma            319795 non-null  int64  
 16  KidneyDisease     319795 non-null  int64  
 17  SkinCancer        319795 non-null  int64  
dtypes: float64(4), int64(14)
memory usage: 43.9 MB


y = pd.DataFrame(df["HeartDisease"], columns=["HeartDisease"])


#Codificador nominal
label_vars = ["Smoking", "AlcoholDrinking", "Stroke", "DiffWalking", "Sex", "Race", "Diabetic",
              "PhysicalActivity", "Asthma", "KidneyDisease", "SkinCancer"]
new_label_vars = ["SmokingYes", "SmokingNo", "AlcoholDrinkingNo", "AlcoholDrinkingYes", "StrokeNo", "StrokeYes", "DiffWalkingNo", 
                  "DiffWalkingYes", "Male", "Female", "White", "Black", "Asian", "American Indian/Alaskan Native",
                 "Other", "Hispanic", "DiabeticYes", "DiabeticNo", "PhysicalActivityYes", "PhysicalActivityNo",
                 "AsthmaYes", "AsthmaNo", "KidneyDiseaseNo", "KidneyDiseaseYes", "SkinCancerYes", "SkinCancerNo"]

df_label = df[label_vars]
hoe = OneHotEncoder()
df_label_ohe = pd.DataFrame(hoe.fit_transform(df_label).toarray(), columns=new_label_vars)
df_label_ohe.head()


#Matriz de variables ordinales
ord_vars = ["AgeCategory", "GenHealth"]
df_ord = df[ord_vars]
oe = OrdinalEncoder()
df_ord_oe = pd.DataFrame(oe.fit_transform(df_ord), columns=ord_vars)
df_ord_oe.head()


#Escalo variables numericas
num_vars = ["BMI", "PhysicalHealth", "MentalHealth", "SleepTime"]
df_num = df[num_vars]
df_num.describe()


scaler = MinMaxScaler()
scaler_st = StandardScaler()
df_num_scaled = pd.DataFrame(scaler.fit_transform(df_num), columns=num_vars)
#df_num_scaled = pd.DataFrame(scaler_st.fit_transform(df_num), columns=num_vars)
df_num_scaled.head()


df_num_scaled.describe()


df_merged = y.merge(df_label_ohe, left_index=True, right_index=True)
df_merged = df_merged.merge(df_ord_oe, left_index=True, right_index=True)
df_merged = df_merged.merge(df_num_scaled, left_index=True, right_index=True)


df_merged.head()


#DIVISION datos
train, test = train_test_split(df_merged, test_size=0.3, random_state=123456)
X_train = train.iloc[:,1:]
X_test = test.iloc[:,1:]
y_train = train.iloc[:,0]
y_test = test.iloc[:,0]
y_train.value_counts()

0    204728
1     19128
Name: HeartDisease, dtype: int64


y_test.value_counts()

0    87694
1     8245
Name: HeartDisease, dtype: int64


#Eliminar algunos y=0


correlation = train.corr().round(2)
plt.figure(figsize = (12,7))
sns.heatmap(correlation, annot = True, cmap = 'Blues')

<AxesSubplot:>


fig = plt.figure(figsize=(17,13))
sns.set_palette("Blues")
plt.figure(figsize=(14,7))
fig.suptitle("Correlation of features vs HearDissease")
abs(correlation['HeartDisease']).sort_values()[:-1].plot.barh()
plt.show()

<Figure size 1224x936 with 0 Axes>


from sklearn.decomposition import PCA


pca = PCA(n_components=12) #98% varainza
pca.fit(X_train)
matriz_cov = pd.DataFrame(pca.components_, columns = X_train.columns)
matriz_cov.head()


print("Explained variance ratio")
print(pca.explained_variance_ratio_)
print(sum(pca.explained_variance_ratio_))

Explained variance ratio
[0.7458427  0.08719624 0.03107972 0.02597748 0.0201626  0.01383879
 0.01301279 0.01188361 0.01043585 0.00880458 0.00712943 0.00453164]
0.9798954229413571


X_copy = X_train
X_train = pd.DataFrame(pca.transform(X_train), columns=["PCA0", "PCA1", "PCA2", "PCA3", "PCA4", "PCA5", "PCA6", "PCA7"
                                                        , "PCA8", "PCA9", "PCA10", "PCA11"])
X_test = pd.DataFrame(pca.transform(X_test), columns=["PCA0", "PCA1", "PCA2", "PCA3", "PCA4", "PCA5", "PCA6", "PCA7"
                                                        , "PCA8", "PCA9", "PCA10", "PCA11"])


X_train.head()


pca3 = PCA(n_components=3)
result = pd.DataFrame(pca3.fit_transform(X_train), columns=["PCA0", "PCA1", "PCA2"])
#result = result * -1

y = pd.DataFrame(y_train, columns=["HeartDisease"])
y.HeartDisease =pd.Categorical(y.HeartDisease)
my_color=y.HeartDisease.cat.codes

fig = plt.figure(figsize = (15,15))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(result['PCA0'], result['PCA1'], result['PCA2'], c=my_color, cmap="Set2_r", s=60)
 
# make simple, bare axis lines through space:
xAxisLine = ((min(result['PCA0']), max(result['PCA0'])), (0, 0), (0,0))
ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r')
yAxisLine = ((0, 0), (min(result['PCA1']), max(result['PCA1'])), (0,0))
ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r')
zAxisLine = ((0, 0), (0,0), (min(result['PCA2']), max(result['PCA2'])))
ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r')
 
# label the axes
ax.set_xlabel("PC1")
ax.set_ylabel("PC2")
#ax.set_zlabel("PC3")
ax.set_title("PCA 3D")
plt.show()


from sklearn.cluster import DBSCAN


dbs = DBSCAN(eps=0.2).fit(result)
clusters_DBS = dbs.labels_


pca3 = PCA(n_components=3)
result = pd.DataFrame(pca3.fit_transform(X_train), columns=["PCA0", "PCA1", "PCA2"])
#result = result * -1

y = pd.DataFrame(clusters_DBS, columns=["clusters_DBS"])
y.clusters_DBS =pd.Categorical(y.clusters_DBS)
my_color=y.clusters_DBS.cat.codes

fig = plt.figure(figsize = (20,20))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(result['PCA0'], result['PCA1'], result['PCA2'], c=my_color, cmap="Set2_r", s=60)
 
# make simple, bare axis lines through space:
xAxisLine = ((min(result['PCA0']), max(result['PCA0'])), (0, 0), (0,0))
ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r')
yAxisLine = ((0, 0), (min(result['PCA1']), max(result['PCA1'])), (0,0))
ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r')
zAxisLine = ((0, 0), (0,0), (min(result['PCA2']), max(result['PCA2'])))
ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r')
 
# label the axes
ax.set_xlabel("PC1")
ax.set_ylabel("PC2")
#ax.set_zlabel("PC3")
ax.set_title("clusters_DBS")
plt.show()


from sklearn.cluster import KMeans


km = KMeans(n_clusters=65, n_init=2, algorithm="auto", random_state=123456)
pred_train = km.fit_predict(X_train)
#print(classification_report(y_train, pred_train))
#print(confusion_matrix(y_train, pred_train))


pca3 = PCA(n_components=3)
result = pd.DataFrame(pca3.fit_transform(result), columns=["PCA0", "PCA1", "PCA2"])
#result = result * -1

y = pd.DataFrame(pred_train, columns=["clusters_km"])
y.clusters_km =pd.Categorical(y.clusters_km)
my_color=y.clusters_km.cat.codes

fig = plt.figure(figsize = (15,15))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(result['PCA0'], result['PCA1'], result['PCA2'], c=my_color, cmap="Set2_r", s=60)
 
# make simple, bare axis lines through space:
xAxisLine = ((min(result['PCA0']), max(result['PCA0'])), (0, 0), (0,0))
ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r')
yAxisLine = ((0, 0), (min(result['PCA1']), max(result['PCA1'])), (0,0))
ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r')
zAxisLine = ((0, 0), (0,0), (min(result['PCA2']), max(result['PCA2'])))
ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r')
 
# label the axes
ax.set_xlabel("PC1")
ax.set_ylabel("PC2")
#ax.set_zlabel("PC3")
ax.set_title("clusters_KMeans")
plt.show()


from sklearn import linear_model


log = linear_model.LogisticRegression(solver="liblinear", penalty="l1", C=0.01)
log.fit(X_train, y_train)
log.coef_

array([[-0.32124228,  0.46880403,  0.52303208,  0.00725687,  0.27445878,
         0.55967098, -0.02702171, -0.27818591,  0.04102488,  0.        ,
        -0.11496308,  0.39287858]])


pred = log.predict(X_test)
print(classification_report(y_test, pred))

              precision    recall  f1-score   support

           0       0.92      0.99      0.95     87694
           1       0.51      0.06      0.10      8245

    accuracy                           0.91     95939
   macro avg       0.71      0.53      0.53     95939
weighted avg       0.88      0.91      0.88     95939


print(confusion_matrix(y_test, pred))

[[87230   464]
 [ 7765   480]]


from sklearn.neighbors import KNeighborsClassifier


knn = KNeighborsClassifier()
knn.fit(X_train, y_train)

KNeighborsClassifier()


print('Accuracy of K-NN classifier on training set: {}'
     .format(knn.score(X_train, y_train)))

Accuracy of K-NN classifier on training set: 0.9253627331856193


pred = knn.predict(X_test)
print(classification_report(y_test, pred))
print(confusion_matrix(y_test, pred))

              precision    recall  f1-score   support

           0       0.92      0.98      0.95     87694
           1       0.34      0.12      0.18      8245

    accuracy                           0.90     95939
   macro avg       0.63      0.55      0.56     95939
weighted avg       0.87      0.90      0.88     95939

[[85807  1887]
 [ 7262   983]]


from sklearn.tree import DecisionTreeClassifier


tree = DecisionTreeClassifier()
tree.fit(X_train, y_train)

DecisionTreeClassifier()


pred = tree.predict(X_test)
print(classification_report(y_test, pred))

              precision    recall  f1-score   support

           0       0.93      0.93      0.93     87694
           1       0.23      0.23      0.23      8245

    accuracy                           0.87     95939
   macro avg       0.58      0.58      0.58     95939
weighted avg       0.87      0.87      0.87     95939


print(confusion_matrix(y_test, pred))

[[81320  6374]
 [ 6333  1912]]


from sklearn.ensemble import RandomForestClassifier


rf = RandomForestClassifier()
rf.fit(X_train, y_train)

RandomForestClassifier()


pred = rf.predict(X_test)
print(classification_report(y_test, pred))

              precision    recall  f1-score   support

           0       0.92      0.96      0.94     87694
           1       0.29      0.16      0.21      8245

    accuracy                           0.89     95939
   macro avg       0.61      0.56      0.57     95939
weighted avg       0.87      0.89      0.88     95939


print(confusion_matrix(y_test, pred))

[[84484  3210]
 [ 6933  1312]]


from sklearn import svm


svm = svm.SVC()
svm.fit(X_train, y_train)

SVC()


pred = svm.predict(X_test)
print(classification_report(y_test, pred))

              precision    recall  f1-score   support

           0       0.91      1.00      0.96     87694
           1       0.67      0.00      0.00      8245

    accuracy                           0.91     95939
   macro avg       0.79      0.50      0.48     95939
weighted avg       0.89      0.91      0.87     95939


from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, RepeatedStratifiedKFold


#LOG
penalty = ["l1", "l2"]
solvers = ['newton-cg', 'lbfgs', 'liblinear']
c_values = [100, 10, 1.0, 0.1, 0.01]
log_grid = dict(solver=solvers,penalty=penalty,C=c_values)

#KNN
n_neighbors = range(1, 5, 1)
weights = ['uniform', 'distance']
metric = ['euclidean', 'manhattan', 'minkowski']
knn_grid = dict(n_neighbors=n_neighbors,weights=weights,metric=metric)

#TREE
criterion = ["gini", "entropy"]
max_depth = range(1,30,2)
max_features = range(1, 5, 1)
tree_grid = dict(criterion=criterion, max_depth=max_depth, max_features=max_features)

#RF
n_estimators = [10, 100, 300, 500, 1000]
criterion = ["gini", "entropy"]
max_depth = range(1,20,2)
max_features = range(1, 5, 1)
rf_grid = dict(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth, max_features=max_features)

#SVM
kernel = ["linear","poly", "rbf", "sigmoid"]
degree = range(3,6,1)
C = [50, 10, 1.0, 0.1, 0.01]
gamma = ['scale']
svc_grid = dict(kernel=kernel,degree=degree,C=C,gamma=gamma)


cols = ["Optimization", 'LOG', 'KNN','TREE','RF','SVM']
results = pd.DataFrame(columns=cols)
results["Optimization"] = ["Standard","RandomSearch"]
results = results.set_index(["Optimization"])
results["LOG"] = [0,0]
results["KNN"] = [0,0]
results["TREE"] = [0,0]
results["RF"] = [0,0]
results["SVM"] = [0,0]

results.head()


models = [log, knn, tree, rf, svm]
grids = [log_grid, knn_grid, tree_grid, rf_grid, svc_grid]


col = 0
for model in models:
    #model.fit(X_train,y_train.values.ravel())
    results.iloc[0,col] = model.score(X_test,y_test)
    col += 1
results.head()


for ind in range(0,len(models)):
    print(ind)


from scipy.stats import randint as sp_randint
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

col = 0
for ind in range(0,len(models)):
    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
    n_iter_search = 30
    random_search = RandomizedSearchCV(models[col], param_distributions=grids[col], n_iter=n_iter_search, cv=cv)
    random_search.fit(X_train,y_train)
    results.iloc[1,col] = random_search.score(X_test,y_test)
    col += 1
    modelo = results.columns[ind]
    print('Model: %s' % modelo)
    print('Best Score: %s' % random_search.best_score_)
    print('Best Hyperparameters: %s' % random_search.best_params_)


results.iloc[1,0] = 0.91497
results.iloc[1,1] = 0.91068
results.iloc[1,2] = 0.91460
results.iloc[1,3] = 0
results = results[["LOG", "KNN", "TREE", "RF", "SVM"]]
results.head()


from sklearn.metrics import roc_auc_score, recall_score
from sklearn import tree


#LOG_OPT
log_opt = linear_model.LogisticRegression(solver="liblinear", penalty="l1", C=0.01)
log_opt.fit(X_train, y_train)

#KNN_OPT
knn_opt = KNeighborsClassifier(weights="uniform", n_neighbors=4, metric="manhattan")
knn_opt.fit(X_train, y_train)

#TREE_OPT
tree_opt = tree.DecisionTreeClassifier(max_features=2, max_depth=5, criterion="entropy")
tree_opt.fit(X_train, y_train)

#RF_OPT
rf_opt = RandomForestClassifier(weights="uniform", n_neigbors=4, metric="manhattan")
rf_opt.fit(X_train, y_train)

#SVM_OPT
svm_opt = svm.SVC()
svm_opt.fit(X_train, y_train)

DecisionTreeClassifier(criterion='entropy', max_depth=5, max_features=2)


models_opt = ["log_opt", "knn_opt", "tree_opt", "rf_opt", "svm_opt"]
cols = ["Metric", 'LOG_OPT', 'KNN_OPT','TREE_OPT','RF_OPT','SVM_OPT']

test_metrics = pd.DataFrame(columns=cols)
test_metrics["Metric"] = ["ROC","RECALL_SCORE"]
test_metrics = test_metrics.set_index("Metric")
test_metrics["LOG_OPT"] = [0,0]
test_metrics["KNN_OPT"] = [0,0]
test_metrics["TREE_OPT"] = [0,0]
test_metrics["RF_OPT"] = [0,0]
test_metrics["SVM_OPT"] = [0,0]
test_metrics.head()


for model in models_opt:
    print(model)

log_opt
knn_opt
tree_opt


#LOG
y_proba = log_opt.predict_proba(X_test)
roc_log = roc_auc_score(y_test, y_proba[:,1], multi_class="ovr")
test_metrics.iloc[0,0] = roc_log
#KNN
y_proba = knn_opt.predict_proba(X_test)
roc_knn = roc_auc_score(y_test, y_proba[:,1], multi_class="ovr")
test_metrics.iloc[0,1] = roc_knn
#TREE
y_proba = tree_opt.predict_proba(X_test)
roc_tree = roc_auc_score(y_test, y_proba[:,1], multi_class="ovr")
test_metrics.iloc[0,2] = roc_tree
#RF
y_proba = rf_opt.predict_proba(X_test)
roc_rf = roc_auc_score(y_test, y_proba[:,1], multi_class="ovr")
test_metrics.iloc[0,3] = roc_rf
#SVM
y_proba = svm_opt.predict_proba(X_test)
roc_svm = roc_auc_score(y_test, y_proba[:,1], multi_class="ovr")
test_metrics.iloc[0,4] = roc_svm


#LOG
pred = log_opt.predict(X_test)
recall_log = recall_score(y_test, pred)
test_metrics.iloc[1,0] = recall_log
#KNN
pred = knn_opt.predict(X_test)
recall_knn = recall_score(y_test, pred)
test_metrics.iloc[1,1] = recall_knn
#TREE
pred = tree_opt.predict(X_test)
recall_tree = recall_score(y_test, pred)
test_metrics.iloc[1,2] = recall_tree
#RF
pred = rf_opt.predict(X_test)
recall_rf = recall_score(y_test, pred)
test_metrics.iloc[1,3] = recall_rf
#SVM
pred = svm_opt.predict(X_test)
recall_svm = recall_score(y_test, pred)
test_metrics.iloc[1,4] = recall_svm


results.head()


test_metrics.head()

	AgeCategory	GenHealth
0	7.0	1.0
1	12.0	1.0
2	9.0	2.0
3	11.0	3.0
4	4.0	1.0

	BMI	PhysicalHealth	MentalHealth	SleepTime
count	319795.000000	319795.00000	319795.000000	319795.000000
mean	28.325399	3.37171	3.898366	7.097075
std	6.356100	7.95085	7.955235	1.436007
min	12.020000	0.00000	0.000000	1.000000
25%	24.030000	0.00000	0.000000	6.000000
50%	27.340000	0.00000	0.000000	7.000000
75%	31.420000	2.00000	3.000000	8.000000
max	94.850000	30.00000	30.000000	24.000000

	BMI	PhysicalHealth	MentalHealth	SleepTime
0	0.055294	0.100000	1.0	0.173913
1	0.100447	0.000000	0.0	0.260870
2	0.175782	0.666667	1.0	0.304348
3	0.147169	0.000000	0.0	0.217391
4	0.141132	0.933333	0.0	0.304348

	BMI	PhysicalHealth	MentalHealth	SleepTime
count	319795.000000	319795.000000	319795.000000	319795.000000
mean	0.196854	0.112390	0.129946	0.265090
std	0.076737	0.265028	0.265175	0.062435
min	0.000000	0.000000	0.000000	0.000000
25%	0.144996	0.000000	0.000000	0.217391
50%	0.184957	0.000000	0.000000	0.260870
75%	0.234215	0.066667	0.100000	0.304348
max	1.000000	1.000000	1.000000	1.000000

	SmokingYes	SmokingNo	AlcoholDrinkingNo	AlcoholDrinkingYes	StrokeNo	StrokeYes	DiffWalkingNo	DiffWalkingYes	Male	Female	...	KidneyDiseaseNo	KidneyDiseaseYes	SkinCancerYes	SkinCancerNo	AgeCategory	GenHealth	BMI	PhysicalHealth	MentalHealth	SleepTime
0	0.019151	-0.019151	-0.004218	0.004218	0.007545	-0.007545	0.024653	-0.024653	-0.009550	0.009550	...	0.006703	-0.006703	0.021505	-0.021505	-0.995383	-0.060745	-0.000016	-0.008928	0.011148	-0.001824
1	-0.077383	0.077383	0.002168	-0.002168	-0.018793	0.018793	-0.093271	0.093271	0.003333	-0.003333	...	-0.020048	0.020048	0.002552	-0.002552	-0.070731	0.959907	0.014770	0.078094	0.051705	-0.003877
2	-0.398400	0.398400	-0.030358	0.030358	-0.003878	0.003878	0.024100	-0.024100	-0.576936	0.576936	...	0.001879	-0.001879	-0.017144	0.017144	-0.002841	-0.040844	0.000319	-0.007029	-0.022935	-0.001821
3	-0.546491	0.546491	-0.050640	0.050640	-0.012316	0.012316	-0.103300	0.103300	0.368886	-0.368886	...	-0.006065	0.006065	0.013084	-0.013084	-0.028119	-0.162729	0.000444	0.054759	0.070041	-0.002484
4	0.155523	-0.155523	0.036075	-0.036075	-0.027498	0.027498	-0.229113	0.229113	-0.154970	0.154970	...	-0.030436	0.030436	0.025831	-0.025831	-0.009881	-0.181803	0.019432	0.099883	0.025065	-0.000755

Proyecto Machine Learning¶

Análisis EDA¶

Lectura de datos¶

Descripción de las variables¶

Valores faltantes¶

Variable de estudio "HeartDisease"¶

Variables numéricas¶

Variables nominales¶

Conversión variables categóricas¶

Variables ordinales¶

Conversion variables ordinales¶

Codificación de variables¶

Codificación nominal¶

Codificación ordinal¶

Codificacion numérica¶

División train/test¶

Resample¶

Reducción de la dimensionalidad¶

Correlación¶

PCA¶

Conclusión¶

Plot 3D PCA¶

Aprendizaje no supervisado¶

DBSCAN¶

KMeans¶

Aprendizaje supervisado¶

Logistic Regression¶

KNN¶

Decision Tree¶

Random Forest¶

SVM¶

Ajuste de hiperparámetros del modelo¶

RandomizedSearchCV¶

Métricas de evaluación¶

ROC_AUC_SCORE¶

RECALL_SCORE¶

Evaluación de modelos¶

	HeartDisease	BMI	Smoking	AlcoholDrinking	Stroke	PhysicalHealth	MentalHealth	DiffWalking	Sex	AgeCategory	Race	Diabetic	PhysicalActivity	GenHealth	SleepTime	Asthma	KidneyDisease	SkinCancer
0	No	16.60	Yes	No	No	3.0	30.0	No	Female	55-59	White	Yes	Yes	Very good	5.0	Yes	No	Yes
1	No	20.34	No	No	Yes	0.0	0.0	No	Female	80 or older	White	No	Yes	Very good	7.0	No	No	No
2	No	26.58	Yes	No	No	20.0	30.0	No	Male	65-69	White	Yes	Yes	Fair	8.0	Yes	No	No
3	No	24.21	No	No	No	0.0	0.0	No	Female	75-79	White	No	No	Good	6.0	No	No	Yes
4	No	23.71	No	No	No	28.0	0.0	Yes	Female	40-44	White	No	Yes	Very good	8.0	No	No	No

	SmokingYes	SmokingNo	AlcoholDrinkingNo	StrokeNo	StrokeYes	DiffWalkingNo	DiffWalkingYes	Male	Female	...	DiabeticYes	DiabeticNo	PhysicalActivityYes	PhysicalActivityNo	AsthmaYes	AsthmaNo	KidneyDiseaseNo	SkinCancerYes	SkinCancerNo
0	0.0	1.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0
1	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	0.0	...	1.0	0.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0
2	0.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	1.0	...	0.0	1.0	0.0	1.0	0.0	1.0	1.0	1.0	0.0
3	1.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	...	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0
4	1.0	0.0	1.0	1.0	0.0	0.0	1.0	1.0	0.0	...	1.0	0.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0

	PCA0	PCA1	PCA2	PCA3	PCA4	PCA5	PCA6	PCA7	PCA8	PCA9	PCA10	PCA11
0	-3.393277	-1.008469	0.335485	-0.979057	-0.069551	-0.102595	-0.118780	0.089390	0.024807	-0.289164	-0.034793	-0.027409
1	2.407848	1.540871	-0.933948	-0.053609	0.474325	-0.856266	-0.533445	-0.055451	-0.418670	-0.089638	-0.114906	0.001112
2	-1.620597	1.345584	-0.099757	0.604758	-0.744438	0.166127	0.615468	-0.830547	-0.761857	-0.155150	-0.108000	-0.100158
3	-1.340652	-1.819618	0.379762	-0.753022	0.137317	0.008939	-0.126913	-0.031590	0.077009	-0.243133	-0.046609	-0.000147
4	2.646283	-1.479043	-0.852333	0.062067	0.077038	-0.044474	0.762354	0.317375	0.228916	0.148232	-0.056153	0.733752

	LOG	KNN	TREE	RF	SVM
Optimization
Standard	0.914227	0.904637	0.867843	0.894277	0.914091
RandomSearch	0.000000	0.000000	0.000000	0.000000	0.000000

	LOG_OPT	KNN_OPT	TREE_OPT
Metric
ROC	0.825160	0.686528	0.756411
RECALL_SCORE	0.058217	0.072165	0.010916

	SmokingYes	SmokingNo	AlcoholDrinkingNo	StrokeNo	StrokeYes	DiffWalkingNo	DiffWalkingYes	Male	Female	...	DiabeticYes	DiabeticNo	PhysicalActivityYes	PhysicalActivityNo	AsthmaYes	AsthmaNo	KidneyDiseaseNo	SkinCancerYes	SkinCancerNo
0	0.0	1.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0
1	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	0.0	...	1.0	0.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0
2	0.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	1.0	...	0.0	1.0	0.0	1.0	0.0	1.0	1.0	1.0	0.0
3	1.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	...	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0
4	1.0	0.0	1.0	1.0	0.0	0.0	1.0	1.0	0.0	...	1.0	0.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0

	SmokingYes	SmokingNo	AlcoholDrinkingNo	StrokeNo	StrokeYes	DiffWalkingNo	DiffWalkingYes	Male	Female	...	DiabeticYes	DiabeticNo	PhysicalActivityYes	PhysicalActivityNo	AsthmaYes	AsthmaNo	KidneyDiseaseNo	SkinCancerYes	SkinCancerNo
0	0.0	1.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0
1	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	0.0	...	1.0	0.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0
2	0.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	1.0	...	0.0	1.0	0.0	1.0	0.0	1.0	1.0	1.0	0.0
3	1.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	...	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0
4	1.0	0.0	1.0	1.0	0.0	0.0	1.0	1.0	0.0	...	1.0	0.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0